Concurrent to our work, [1]} proposes an intra-image clustering step of pixel embeddings before applying a contrastive loss on the identified pixel groups to segment images.
However, as they are reliant on combining the former with an image-level loss, it is not well-suited for more generic images with multiple objects where image-level and pixel-level semantics do not match.
In contrast, our method uses a single clustering objective that is generalized for the dense setting, but that also works on object-centric images.
Furthermore, by leveraging ViT's natural ability to direct its attention to objects, we do not require any external saliency generator like [2]}.
